Implementation of Naive Bayes Algorithm for Early Detection of Stunting Risk
DOI:
https://doi.org/10.30871/jaic.v9i2.9144Keywords:
Naive Bayes Algorithm, Early Detection, Stunting Risk, Predictive ModelAbstract
This study aimed to develop an early detection model for stunting risk in children in Kuningan Regency using the Naïve Bayes algorithm. The model used 3,155 data with a division of 50% training data and 50% testing data, utilizing five predictor variables: gender, age, weight, height, and nutritional intake. The results demonstrated an accuracy of 66.8%, precision of 62.4%, and recall of 69.5%, indicating that the model performs adequately but requires further refinement to enhance predictive quality. Improvements can be achieved by incorporating additional variables, such as environmental factors, sanitation, and maternal nutritional status, as well as optimizing data preprocessing techniques. The findings provide a scientific basis for the Kuningan Regency Health Office to design targeted intervention strategies, including regular screening programs, specific nutritional interventions, and community health education. Effective implementation of these strategies requires collaborative efforts among local government, community health centers (puskesmas), integrated health posts (posyandu), and other stakeholders to ensure a holistic and sustainable approach to stunting prevention. This study highlights the potential of data-driven models in supporting evidence-based public health policies and interventions.
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